AISM 52, 351-366
(Received May 13, 1997; revised August 28, 1998)
Abstract. Asymptotic biases and variances of M-, L- and R-estimators of a location parameter are compared under epsilon-contamination of the known error distribution F0 by an unknown (and possibly asymmetric) distribution. For each epsilon-contamination neighborhood of F0, the corresponding M-, L- and R-estimators which are asymptotically efficient at the least informative distribution are compared under asymmetric epsilon-contamination. Three scale-invariant versions of the M-estimator are studied: (i) one using the interquartile range as a preliminary estimator of scale: (ii) another using the median absolute deviation as a preliminary estimator of scale; and (iii) simultaneous M-estimation of location and scale by Huber's Proposal 2. A question considered for each case is: when are the maximal asymptotic biases and variances under asymmetric epsilon-contamination attained by unit point mass contamination at \infty? Numerical results for the case of the epsilon-contaminated normal distribution show that the L-estimators have generally better performance (for small to moderate values of epsilon) than all three of the scale-invariant M-estimators studied.
Key words and phrases: Robust estimation, M-, L- and R-estimators, asymptotic biases, asymptotic variances, asymmetric contamination.
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